Network Based Technology Roadmapping for Future Markets: Case of 3D Printing
Bibliographic record
Abstract
A clear and understandable Technology Roadmap (TRM) is necessary to planning and navigating change in the product development process. The fabric of the 3D printing landscape is complex and difficult to understand from single snapshot approach and a TRM is only as useful as it is understandable and easily communicable. Successful Technology Roadmapping involves expert industry analysis, technology expertise, and visual story telling. This research builds upon the principles of existing Technology Roadmapping practices to develop models that apply to the consumer market of the 3D content-to-print industry. In managing the involved complexity, multiple tools and methods have been explored, focusing on the efficacy and legibility of TRM’s. Literature review, analysis of market forces, patent analysis, and quality functional deployment (QFD) were used to establish current and future market drivers and subsequent product features. Technology forecasting and scenario analysis were then used to create product portfolios for 3D content manufactures. The application and research explored creating two future product scenario’s; a low cost (LC) product that would maintain the current state of the art performance metrics tailored to the mass market consumer and a high performance (HP) product that would continue to push the capability of the at home manufacturing performance. These bifurcating foci further complicate the visual illustration of these roadmaps. An exercise in visual display of a large blanket of networks and relationships has led to a powerful tool used to identify future reach and impacts of early technological investments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".